It is often desirable to measure certain performance characteristics of a powertrain system of a vehicle in order to establish a baseline level against which a differently configured vehicle can be gauged. For example, in the case where a component of the vehicle is proposed to be changed, it is desirable to be able to objectively understand the corresponding changes in performances. For example, in the commercial vehicle (e.g., truck) context, it is known to have a number of transmission choices from different manufacturers, so vehicle owners may consider what fuel economy benefits, if any, would be available were the truck to be configured with a transmission from a different manufacturer. In the case of a driver-controlled vehicle, however, it is often difficult to conduct rigorous and meaningful “A versus B” testing due to the effects of driving behavior as well as environmental variability, such as traffic, changes in the load carried by the vehicle, and the like. Accordingly, such comparisons are frequently met with a healthy dose of skepticism.
One approach for gauging the impact of a proposed vehicle change is to conduct a simulation, for example, a simulation to evaluate and analyze fuel economy (FE) performance. However, the results of such a simulation are routinely questioned for a number of reasons.
First, vehicle configurations that are set up for use in the simulation do not often match real-world conditions. For example, in the case of a delivery truck, the vehicle weight can change over time due to the loading and unloading of cargo along a delivery route. As known, vehicle weight strongly influences fuel economy, so accurate modeling of this variable is important. However, it is difficult if not nearly impossible to have exact tracking of weight changes for the simulation setup.
Second, the operational duty cycle used in a conventional fuel economy simulation is typically limited to a road grade and speed versus distance profile. This profile is typically referred to as a metric duty cycle. With the adoption of such duty cycles, conventional simulators will not produce accurate results unless certain information is known with some degree of accuracy, for example vehicle inertia information as well as other driving environmental factors, such as wheel rolling resistance and headwind velocity.
Third, conventional metric duty cycles do not take into account traffic conditions, particularly the driver's response to such conditions, which can cause unexpected “stop and go” operation of the vehicle along the driver's route, which can adversely effect fuel economy. In sum, conventional fuel economy simulators are of limited informational value because a number of underlying assumptions do not accurately reflect real-world driving conditions.
In view of the deficiencies in accurately predicting performance due to proposed vehicle equipment changes, vehicle owners are, perhaps rightfully, reluctant to move forward in making equipment upgrades and/or deciding to configure vehicles with certain equipment. This reluctance is particularly true where the cost/benefit or savings, relative to a baseline vehicle, cannot be clearly demonstrated. This reluctance is only amplified when considering fleet vehicle owners, who may control a plurality of vehicles. In the case of fuel economy, it is therefore not uncommon for vehicle fleet owners to make vehicle equipment decisions based on their own knowledge and experience as to their particular routes, driver behavior and vehicle configurations. This human-based judgment is sometimes qualified as one involving “my route, my driver, and my truck”. In the absence of a tailored approach that quantitatively accounts for “my route, my driver, and my truck”, a vehicle owner would rely on subjective analysis of performance, likely discount, if not disregard entirely, any performance (e.g., fuel economy) pertaining to a proposed equipment change.
There is therefore a need for a system and method for simulating the performance of a vehicle that minimizes or eliminates one or more of the shortcomings described above.